Do we really need to remind anybody about the fact that email (and email marketing) isn’t going anywhere anytime soon? If we did, we’d just flash this study by Radicati-koncernen on them, containing such zingers as…

I slutet av 2019 kommer antalet e-postanvändare i världen att ha ökat till över 2,9 miljarder. Över en tredjedel av världens befolkning kommer att använda e-post i slutet av 2019.

For those of us who work in email? Stats like that are pretty temptacious, as the kids say. But dagens email isn’t your mom or dad’s email. Den continuing success of email lies, in large part, to how its ability to evolve. Going mobile put email into a lot more pockets, for instance. 

Nu, med AI-relaterad teknik, kan din e-post campaigns bli ännu mer exakt, engagerande och effektiv än den någonsin har varit.

Den arrival of email AI? That’s so 2018 

At the end of 2018, PwC said it had surveyed U.S. execs, and found 27% of them claiming to be redan implementing AI in multiple areas. 

On the global front, 30% av företagen i hela världen will be using AI in at least one of their sales processes by 2020. But only 17% of email marketers considering automation tools gave any thought to incorporating AI.

The laggards might not realize the impact AI has already had on the email ecosystem. One very visible example was how Gmail handles email classification using Natural Language Processing (NLP) to filter incoming emails as Primary, Social, or Promotions messages. 

Here’s a ganska bra förklaring of how NLP does its job, presented as a primer for coders who want to hack up a spam filter. But if you aren’t interested in all the plumbing, that’s cool. One thing worth remembering, though? NLP and machine learning are only branches of the bigger, broader category “AI” and have specific goals.  

• NLP is intended to read, decipher, understand, and make sense of human language in a manner that’s useful in machine-human interaction. 

• Maskininlärning innebär tillämpning av algoritmer och statistiska modeller så att datorer kan fatta beslut och utföra uppgifter utan uttryckliga instruktioner genom att känna igen mönster i data och dra slutsatser.

Just nu finns det flera verktyg och taktiker där NLP och maskininlärning används för att förbättra e-postprogram. Låt oss titta på några av de platser där du kan integrera dem i din campaigns, ska vi ...?

Testning

With machine learning, you can now execute flerarmat bandit-test. If you’re used to split testing, brace yourself: Now you’ll be able to run tests kontinuerligt and put your findings to work omedelbart. Over time, you’ll gradually optimize your results, and simultaneously be able to test content and messaging while also sending your best-performing variant out to prospects or customers.

How’s it done? You set up a campaign and a few email variations, and machine learning does the rest, running tests throughout your campaign and fine-tuning it on the basis of test data. What can you test? Pretty much anything you’re already testing, from copy to design to images to timing. 

Copywriting

Maskininlärning och NLP - och dess kusin Natural Language Genration (NLG) - utnyttjas av flera leverantörer för att leverera lösningar som faktiskt kan generera ämnesrader och annan text.

Take a company like Persado, for instance: Its “message machine” applies its grasp of natural language to create copy that speaks in the marketer’s “brand voice,” leveraging a huge database of tagged and scored works in 25 languages, a database that evolves over time as machine learning delivers insights (and makes judgments) about which messages hold the most appeal for your target audience.

Beröringspunkt, as another example, compares your subject line against a database of 21 billion emails, as well as industry trends, to predict its likely impression, click and conversion rates.

Rasa.io automated the newsletter creation process, and uses machine learning to optimize content based on each recipient’s behaviors to provide 1:1 personalization that’s “skräddarsydda efter dina prenumeranters unika intressen och personligheter, utan den tid det tar att manuellt sammanställa dina e-postmeddelanden."

Uppdrag

Want to pull off a little real-time content optimization to drive engagement? Cordial says it can “ingest and process customer event, behavior, and purchase data from virtuellly any source,” so messages can be dispatched across multiple channels, based on upp-till-rätt-detta-instant behavioral data. So onboarding, re-engagement campaigns, and other triggered emails can be aligned with what they’re interested in this very moment.

Another way to engage? Add a personal touch. Well, a virtual personal touch: Conversica proudly claims to deliver “personalized human touch at scale” through AI sales assistants that reach out to a user within minutes of him or her showing interest in your brand or inventory via email or SMS. 

If you’re worried the “conversation” reads like robo-copy, they claim the AI “empathizes” effectively by analyzing replies to tailor the right responses.  Moreover, the platform isn’t intended simply for initial engagement or onboarding but can handle routine dialogues throughout the entire customer journey.

Segmentering

For companies investing in customer data management platforms, being able to milk the greatest possible insight and benefits from big data to deliver highly personalized user experiences, especially in email, is an obvious concern. 

A machine learning solution that’s connected to these potentially enormous pools of data can do insightful segmentation in ways no human being – or boiler room full of human beings – ever could, making continual adjustments and uncovering new associations, even generating product new segments where none were visible before.  SimMachines is one of these providers, calling their particular flavor “dynamic predictive segmentation.” 

Prediktiv leverans

If you haven’t heard of it before, that’s because det är en ny rynka in applying machine learning to email. By analyzing the behavior of trillions of emails, predictive analytics and machine learning are able to optimize delivery and the overall health of an email program.

This means real-time insights are available about deliverability and performance issues, problems can be identified before they happen, and data-driven recommendations can be made about how to optimize engagement and performance.  Outages can be avoided – while ROI is maximized.

And if you’ll allow just one self-plug? It’s new till game because this platform, SparkPost-signaler, is the first and only email intelligence platform of its kind in the industry, and we’re proud to be offering it.

Det är en AI-för-e-post-explosion

Detta är bara några av de områden där AI, NLP och maskininlärning i dag påverkar email marketing. Om du tror att det är toppen av isberget - eller den första droppen genom dammluckorna - så har du rätt.

One way to see how feverish a new technology segment is getting is to see how many companies and startups have hung out a shingle, using investor or job sites like AngelList. Right now, a search for “email AI” there visar över 600 företag in the space, and there’ll be more to come.

In other words, there’ll eventually be an AI add-on for every facet of your email program.  In the meantime? Putting today’s existing AI tools to work already offers mycket of potential for discovering how NLP and machine learning can improve the way you’re using a veteran marketing channel that’s just as leading-edge as ever.